Peter Norvig on statistical learning as science

It’s interesting that causality doesn’t come up until the comments, when Stephen Conn discusses Galileo. Now, you can get a long way in science without thinking about causality, or only thinking about causality in a very simple way — that’s the basis for the success of statistics over the last century. And indeed, if you only care about prediction without intervention, it’s not necessary to tease out cause. But if you care about what happens after an intervention, you need to know something about the causal structure, and at this point in history, finding causal structure is not something machines are good at doing. (To be fair, humans aren’t always good at this either.) The philosophical difficulty is that almost everything is an intervention — changing Google’s search algorithm is an intervention — though often, the intervention changes the system by a negligible amount. Intervention is a continuum, and given the current state of knowledge in statistics and machine learning, this is something that should keep statisticians awake at night.